Today's industrial development has made a rapid growth by meeting the current requirements of almost every sector of the society. Most developed industries including the communication industry, high tech industries, equipment industries and others have made a high jump in terms of technology advancement. With increased competition all these industries are now more focused towards the pre analyses of customer's requirements and aligning their operational processes to those. This focus on customer centricity is required to differentiate their products and services and excel in customer experience. With the constant shift in industry dynamics, organizations need to focus on new opportunities, key result areas (KRAs) and key performance indicators (KPIs) to stay ahead of the competition and as businesses have expanded, and the complexities in operational systems have also grown manifold leading to challenges in getting a uniform view of the organization across the common KPIs and KRAs. With the increasing data volumes from multiple systems, all these industries have to be more and more vigilant in checking the overall performance of the decision making system while reducing the probability of risk and loss.
Data warehousing, the creation of an enterprise wide data source, is the first step towards managing the large volumes of customer related data. The data warehousing is becoming an integral part of many information delivery systems because it provides a single, central location where a reconciled version of data extracted from a wide variety of operational source is stored for performing analyses. There are a number of existing solutions that provide generic data warehouse and analytical capabilities. Current solutions do not provide an industry specific prebuilt analytics that are pre-integrated with a data warehouse and a data services solution. This pre-integrated solution can cater to end to end business intelligence (BI) needs of the organizations in collating information across the enterprise to provide analytics and at the same time provide real time access of these analytics to operational systems. Current solutions also do not provide such pre-integrated analytics in the business areas of subscriber network experience, product portfolio performance, churn-out analytics, retail store performance, dynamic brand sentiment measurement, etc. In absence of such pre-integrated solutions, organizations currently are incurring additional costs and time in building similar functionalities.
Therefore, there is a need of a system which is capable of providing support for business analyses and intelligent operations through integration with rules engine and operational processes. The system should also be flexible in terms of service model implementation and maintenance.